Cryptosystem designers frequently assume that secrets will be manipulated in closed, reliable computing environments. Unfortunately, actual computers and microchips leak information about the operations they process. This paper examines specific methods for analyzing power consumption measurements to find secret keys from tamper resistant devices. We also discuss approaches for building cryptosystems that can operate securely in existing hardware that leaks information.

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Differential Power Analysis

This monograph provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks. The application areas are chosen with the following three criteria in mind: (1) expertise or knowledge of the authors; (2) the application areas that have already been transformed by the successful use of deep learning technology, such as speech recognition and computer vision; and (3) the application areas that have the potential to be impacted significantly by deep learning and that have been experiencing research growth, including natural language and text processing, information retrieval, and multimodal information processing empowered by multi-task deep learning.

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Deep Learning: Methods and Applications

This ebook is the first authorized digital version of Kernighan and Ritchie's 1988 classic, The C Programming Language (2nd Ed.). One of the best-selling programming books published in the last fifty years, "K&R" has been called everything from the "bible" to "a landmark in computer science" and it has influenced generations of programmers. Available now for all leading ebook platforms, this concise and beautifully written text is a "must-have" reference for every serious programmers digital library.

As modestly described by the authors in the Preface to the First Edition, this "is not an introductory programming manual; it assumes some familiarity with basic programming concepts like variables, assignment statements, loops, and functions. Nonetheless, a novice programmer should be able to read along and pick up the language, although access to a more knowledgeable colleague will help."

The C programming language

Recently, the hybrid deep neural network (DNN)- hidden Markov model (HMM) has been shown to significantly improve speech recognition performance over the conventional Gaussian mixture model (GMM)-HMM. The performance improvement is partially attributed to the ability of the DNN to model complex correlations in speech features. In this paper, we show that further error rate reduction can be obtained by using convolutional neural networks (CNNs). We first present a concise description of the basic CNN and explain how it can be used for speech recognition. We further propose a limited-weight-sharing scheme that can better model speech features. The special structure such as local connectivity, weight sharing, and pooling in CNNs exhibits some degree of invariance to small shifts of speech features along the frequency axis, which is important to deal with speaker and environment variations. Experimental results show that CNNs reduce the error rate by 6%-10% compared with DNNs on the TIMIT phone recognition and the voice search large vocabulary speech recognition tasks.

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Convolutional neural networks for speech recognition

From the Publisher:
Connectionist Speech Recognition: A Hybrid Approach describes the theory and implementation of a method to incorporate neural network approaches into state-of-the-art continuous speech recognition systems based on Hidden Markov Models (HMMs) to improve their performance. In this framework, neural networks (and in particular, multilayer perceptrons or MLPs) have been restricted to well-defined subtasks of the whole system, i.e., HMM emission probability estimation and feature extraction. The book describes a successful five year international collaboration between the authors. The lessons learned form a case study that demonstrates how hybrid systems can be developed to combine neural networks with more traditional statistical approaches. The book illustrates both the advantages and limitations of neural networks in the framework of a statistical system. Using standard databases and comparing with some conventional approaches, it is shown that MLP probability estimation can improve recognition performance. Other approaches are discussed, though there is no such unequivocal experimental result for these methods. Connectionist Speech Recognition: A Hybrid Approach is of use to anyone intending to use neural networks for speech recognition or within the framework provided by an existing successful statistical approach. This includes research and development groups working in the field of speech recognition, both with standard and neural network approaches, as well as other pattern recognition and/or neural network researchers. This book is also suitable as a text for advanced courses on neural networks or speech processing.

Connectionist Speech Recognition: A Hybrid Approach

A mixed two-dimensional numerical device/circuit simulation analysis of scaled BiCMOS and CMOS circuits is presented. Submicrometer processes with 0.4- mu m design rules operating at a power supply down to 2.8 V are examined. It is shown that when subjected to scaling, conventional BiCMOS maintains its superior performance compared to that of CMOS even at scaled power supplies. >

Low-voltage scaled CMOS and BiCMOS digital circuits

As VLSI technologies scale down, the average die size is expected to remain constant or to slightly increase with each generation. This results in an average increase in the global interconnect lengths. To mitigate their impact, buffer insertion has become the most widely used technique. However, unconstrained buffering is expected to require several hundreds of thousands of global interconnect buffers. This increased number of buffers is destined to adversely impact the chip power consumption. In this paper, an optimal power maze routing and buffer insertion/sizing problem for a two-pin net is formulated, as a shortest paths ranking problem. The pseudopolynomial time bound of the new formulation fits well within the context of the increased number of buffers. In fact, power savings as high as 25% for the 130-nm technology with a 10% sacrifice in delay is achieved. Furthermore, with the advent of dual threshold technologies, power sensitive applications can substantially benefit from adopting dual threshold buffers. Accordingly, the proposed problem formulation is extended to incorporate the selection of the buffer threshold voltage, where a twofold increase in power savings is observed. During the assessment of the impact of technology scaling using a set of MCNC Benchmarks, an average power saving as high as 35% with a 10% sacrifice in delay is observed. In addition, there is a 10% variation in the power savings when accounting for the process variations.

POMR: a power-aware interconnect optimization methodology

In this introductory chapter, a brief description of the power estimation and evaluation for digital circuits is given in the next section This would serve as a back ground for the subsequent chapters Also, the impact of the new power-concious design philosophy on the process design and semiconductor technology is outlined Finally, an overview of the book’s main chapters is provided

Low-Power VLSI Design

The design and optimization of BiCMOS buffer chains and multi level logic circuits are reported. BiCMOS speedup contours are introduced and analytical expressions for the delay are obtained. The speedup contours and the delay expressions were used in the design and optimization of BiCMOS buffer chains. Also, general design guidelines, which can be easily automated, for circuit design in a BiCMOS environment are given. Designing multistage mixed CMOS/BiCMOS buffers, BiCMOS complex logic gates, and multi level CML (current mode logic) gates is also studied. >

Design and optimization of buffer chains and logic circuits in a BiCMOS environment

A low power high performance adder using a conditional sum adder (CSA) architecture and complementary pass logic (CPL) implementation. The adder comprises a plurality of blocks, each block including a conditional sum cell and an output multiplexer. Each block except the first, also comprises a block of 2:1 multiplexers intermediate the conditional sum cell and the output multiplexer. The adder according to the present invention operates with lower power consumption and at greater speed than prior art adder architectures.

Mohamed I. Elmasry

Papers

Low-voltage scaled CMOS and BiCMOS digital circuits

POMR: a power-aware interconnect optimization methodology

Low-Power VLSI Design

Design and optimization of buffer chains and logic circuits in a BiCMOS environment

Low-power high performance adder